Fiducial markers are used in medical imaging technologies for reference. Image processing algorithms have been used to identify such markers in an image. Conventional image processing algorithms use templates limiting identification to a certain orientation and size of an object of interest. Identification of fiducial markers and other objects of interest is important in the field of medicine, since it can facilitate diagnosis and treatment.
The use of deep neural networks can improve on conventional image processing techniques by allowing identification of objects of interest of any size or resolution in an image. While use of deep neural networks seems promising, one challenge of deep networks is their requirement for large and preferably high-resolution training sets. The use of deep neural networks for identification of objects in medical imagery is constrained by the nature of medical images, which are often coarse and few in number. In addition, due to privacy concerns, large data sets of medical images are not freely available. These aspects of medical imagery make it difficult to take advantage of the features of deep neural networks.
Accordingly, there is a need for a new automatic image object detection method to improve object detection performance on medical images or related fields.